Statistical Translation, Heat Kernels and Expected Distances. - Joshua V. Dillon; Yi Mao; Guy Lebanon; Jian Zhang 003 (UAI, 2007)

The Locally Weighted Bag of Words Framework for Document Representation. - Guy Lebanon; Yi Mao; Joshua V. Dillon (J. Mach. Learn. Res., 2007)

Sequential Document Visualization. - Yi Mao; Joshua V. Dillon; Guy Lebanon (IEEE Trans. Vis. Comput. Graph., 2007)

Statistical and Computational Tradeoffs in Stochastic Composite Likelihood. - Joshua V. Dillon; Guy Lebanon (AISTATS, 2009)

A unified optimization framework for robust pseudo-relevance feedback algorithms. - Joshua V. Dillon; Kevyn Collins-Thompson (CIKM, 2010)

Asymptotic Analysis of Generative Semi-Supervised Learning. - Joshua V. Dillon; Krishnakumar Balasubramanian 002; Guy Lebanon (ICML, 2010)

Stochastic Composite Likelihood. - Joshua V. Dillon; Guy Lebanon (J. Mach. Learn. Res., 2010)

Cumulative Revision Map - Seungyeon Kim 001; Joshua V. Dillon; Guy Lebanon (CoRR, 2012)

Deep Variational Information Bottleneck. - Alexander A. Alemi; Ian Fischer; Joshua V. Dillon; Kevin Murphy 002 (ICLR (Poster), 2017)

An Information-Theoretic Analysis of Deep Latent-Variable Models. - Alexander A. Alemi; Ben Poole; Ian Fischer; Joshua V. Dillon; Rif A. Saurous; Kevin Murphy 002 (CoRR, 2017)

TensorFlow Distributions. - Joshua V. Dillon; Ian Langmore; Dustin Tran; Eugene Brevdo; Srinivas Vasudevan; Dave Moore; Brian Patton; Alex Alemi; Matthew D. Hoffman; Rif A. Saurous (CoRR, 2017)

Fixing a Broken ELBO. - Alexander A. Alemi; Ben Poole; Ian Fischer; Joshua V. Dillon; Rif A. Saurous; Kevin Murphy 002 (ICML, 2018)

Uncertainty in the Variational Information Bottleneck. - Alexander A. Alemi; Ian Fischer; Joshua V. Dillon (CoRR, 2018)

Likelihood Ratios for Out-of-Distribution Detection. - Jie Ren 006; Peter J. Liu; Emily Fertig; Jasper Snoek; Ryan Poplin; Mark A. DePristo; Joshua V. Dillon; Balaji Lakshminarayanan (NeurIPS, 2019)

Can you trust your model's uncertainty? Evaluating predictive uncertainty under dataset shift. - Jasper Snoek; Yaniv Ovadia; Emily Fertig; Balaji Lakshminarayanan; Sebastian Nowozin; D. Sculley; Joshua V. Dillon; Jie Ren 006; Zachary Nado (NeurIPS, 2019)

The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks. - Jakub Swiatkowski; Kevin Roth; Bastiaan S. Veeling; Linh Tran; Joshua V. Dillon; Jasper Snoek; Stephan Mandt; Tim Salimans; Rodolphe Jenatton; Sebastian Nowozin (ICML, 2020)

Hydra: Preserving Ensemble Diversity for Model Distillation. - Linh Tran; Bastiaan S. Veeling; Kevin Roth; Jakub Swiatkowski; Joshua V. Dillon; Jasper Snoek; Stephan Mandt; Tim Salimans; Sebastian Nowozin; Rodolphe Jenatton (CoRR, 2020)

Joint Distributions for TensorFlow Probability. - Dan Piponi; Dave Moore; Joshua V. Dillon (CoRR, 2020)

tfp.mcmc: Modern Markov Chain Monte Carlo Tools Built for Modern Hardware. - Junpeng Lao; Christopher Suter; Ian Langmore; Cyril Chimisov; Ashish Saxena; Pavel Sountsov; Dave Moore; Rif A. Saurous; Matthew D. Hoffman; Joshua V. Dillon (CoRR, 2020)

PACm-Bayes: Narrowing the Empirical Risk Gap in the Misspecified Bayesian Regime. - Warren R. Morningstar; Alexander A. Alemi; Joshua V. Dillon (CoRR, 2020)

VIB is Half Bayes. - Alexander A. Alemi; Warren R. Morningstar; Ben Poole; Ian Fischer; Joshua V. Dillon (CoRR, 2020)

Density of States Estimation for Out of Distribution Detection. - Warren R. Morningstar; Cusuh Ham; Andrew G. Gallagher; Balaji Lakshminarayanan; Alexander A. Alemi; Joshua V. Dillon (AISTATS, 2021)

Automatic Differentiation Variational Inference with Mixtures. - Warren R. Morningstar; Sharad M. Vikram; Cusuh Ham; Andrew G. Gallagher; Joshua V. Dillon (AISTATS, 2021)

PACm-Bayes: Narrowing the Empirical Risk Gap in the Misspecified Bayesian Regime. - Warren R. Morningstar; Alex Alemi; Joshua V. Dillon (AISTATS, 2022)

Automatically Bounding the Taylor Remainder Series: Tighter Bounds and New Applications. - Matthew Streeter; Joshua V. Dillon (CoRR, 2022)

Weighted Ensemble Self-Supervised Learning. - Yangjun Ruan; Saurabh Singh; Warren Richard Morningstar; Alexander A. Alemi; Sergey Ioffe; Ian Fischer; Joshua V. Dillon (ICLR, 2023)

SubMix: Learning to Mix Graph Sampling Heuristics. - Sami Abu-El-Haija; Joshua V. Dillon; Bahare Fatemi; Kyriakos Axiotis; Neslihan Bulut; Johannes Gasteiger; Bryan Perozzi; MohammadHossein Bateni (UAI, 2023)

Sharp Taylor Polynomial Enclosures in One Dimension. - Matthew Streeter; Joshua V. Dillon (CoRR, 2023)

VideoPoet: A Large Language Model for Zero-Shot Video Generation. - Dan Kondratyuk; Lijun Yu; Xiuye Gu; José Lezama; Jonathan Huang; Grant Schindler; Rachel Hornung; Vighnesh Birodkar; Jimmy Yan; Ming-Chang Chiu; Krishna Somandepalli; Hassan Akbari; et al. (ICML, 2024)

Sample what you cant compress. - Vighnesh Birodkar; Gabriel Barcik; James Lyon; Sergey Ioffe; David Minnen; Joshua V. Dillon (CoRR, 2024)

Federated Variational Inference: Towards Improved Personalization and Generalization. - Elahe Vedadi; Joshua V. Dillon; Philip Andrew Mansfield; Karan Singhal; Arash Afkanpour; Warren Richard Morningstar (Trans. Mach. Learn. Res., 2024)